Retailers often have databases and warehouses full of thousands upon thousands of products offered for sale, with new product items being added and offered every day. Accordingly, the databases must be updated with these new products in an organized and usable manner. Each existing product and new product item should be categorized within the database so that it can be found by customers for purchase or employees for stocking. The large number of products offered for sale by a merchant makes updating a merchant's product database human labor intensive and costly if manual labor is used in the current methods and systems. On the other hand, computer based systems can pose accuracy problems that is unacceptable in the current market place. There have been traditionally several challenges with classification models correctly identifying key words in order to provide an accurate classification. For example, how to generate a keyword list for each product type, such that the list of keywords contains the useful and important key words to describe the items within the product type. Additionally, there could be many words appearing in the titles or the descriptions of an item that are not important and different words have different importance with regards to classifying the new product item within a product type.
These problems and other problems persist with the use of computers and current computing systems. The disclosed methods and systems herein, provide more efficient and cost effective methods and systems for merchants to keep product databases up to date with new product offerings. More specifically, the disclosed methods and systems involve computer program products for automatically determining key words within item information with product types, and classifying new items within product types within a merchant's database.